The Art And Science Of Forecasting Recessions

This article is probably the most exhaustive and challenging piece I have written. It was worth the effort because understanding the business cycle is crucial to making great investment decisions. To get the full benefit, I urge readers to spend some time reading the background links and watching the videos.

I am going to follow up with another piece describing how I use this information for investment decisions. For now, let us all focus on the method, understanding how and why it has worked so well throughout history.

Background

In May of 2011 I embarked on a search for the best recession forecasting methods. I had been a long-time fan of the ECRI approach. They were still very positive on the economy at the time, and my quest was not driven by their conclusions. I was uncomfortable with the methodology and the lack of transparency. I had many reader suggestions, and I reviewed them all. The criteria were stringent -- "Jeff's Acid Test." The easy winner of this competition was Robert F. Dieli's "Mr. Model." (This article described the competition and the results).

The main conclusion from Bob's work was that there was no imminent recession. This ran counter to some other well-publicized and popular forecasts. Some readers complained in the comments that the history of the forecast included some imperfections. Others disagreed with the methods. The subject was too difficult for simple responses to these questions. I promised to follow up in more detail, but I wanted to do so in a convincing fashion.

A Year Later

A year later, some key elements of my rationale should be even more convincing:

Bob was right -- once again, as so many times before. And he did it in real time, not on a back-tested basis.

Imperfections in real-time forecasting are acceptable -- even desirable. When I see a perfect forecast, it always means that the model has been tweaked and changed to fit all of the past data.

Simple is good. Methods that over-specify the number of variables and numerical trigger points also imply excessive back-fitting and poor predictability.

Theory is important. The model should make sense.

Most recession forecasting models fail because they emphasize weakness. This is backwards. A recession begins at a business cycle peak, something that I explain more carefully here. A recession starts with excessive strength. Seen any of that lately?

Your intuition about the business cycle would be better if you completely forgot the "R" word and took Bob's lead: Substitute "business cycle peak."

The key driver of Bob's forecast is what he calls the "Aggregate Spread." By reviewing results over decades we can see that this method actually provides a warning of about nine months. The image below describes the composition of the spread, using example data from August.

Jeff Miller The most recent aggregate spread is shown below. Just as it did last year, it provides strong evidence that the US economy is not nearing a recession.

Get some popcorn and your favorite beverage and settle back to watch the show. I recently met with Bob Dieli to discuss economic forecasting and to create some videos. The result is an eight-part series in which we discuss each of the recessions of the latter 20th Century. [Thanks to Derek Miller for helping in the production of the videos and producing the key summaries.]

In this first video, Bob and I discuss National Bureau of Economic Research and why their definitions of a recession are important. The nonpartisan NBER looks at both the peaks and troughs of the business cycle to conclude when past recessions have happened, effectively making "autopsies, rather than forecasts" - as Bob says. Therefore, it is important for the Mr. Model to use the same criteria when it forecasts for recessions, providing a clearer picture than other models.